English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Data-driven decision making is becoming an integral part of manufacturingcompanies. Data is collected and commonly used to improve efficiency andproduce high quality items for the customers. IoT-based and other forms ofobject tracking are an emerging tool for collecting movement data ofobjects/entities (e.g. human workers, moving vehicles, trolleys etc.) overspace and time. Movement data can provide valuable insights like processbottlenecks, resource utilization, effective working time etc. that can be usedfor decision making and improving efficiency.  Turning movement data into valuable information for industrial management anddecision making requires analysis methods. We refer to this process as movementanalytics. The purpose of this document is to review the current state of workfor movement analytics both in manufacturing and more broadly.  We survey relevant work from both a theoretical perspective and anapplication perspective. From the theoretical perspective, we put an emphasison useful methods from two research areas: machine learning, and logic-basedknowledge representation. We also review their combinations in view of movementanalytics, and we discuss promising areas for future development andapplication. Furthermore, we touch on constraint optimization.  From an application perspective, we review applications of these methods tomovement analytics in a general sense and across various industries. We alsodescribe currently available commercial off-the-shelf products for tracking inmanufacturing, and we overview main concepts of digital twins and theirapplications.

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective